The Complete Data Analysis Course 2022 with Python

Explore what a database is, define structure query language and its role with relational database management systems, and introduce MongoDB.

Learn how data wrangling transforms and curates data from streaming sources into high-quality, analytics-ready data for downstream use, reducing redundancy.

Implement data wrangling in Python by loading data in a notebook, using pandas in Colab, dropping the first column, and verifying the data shape (94 by 20).

Explore data wrangling with subset selection of columns such as customer ID, customer name, city, postal code, and sales; filter by ranges and compute mean, standard deviation, and unique values.

Master data wrangling techniques in Python by filtering data (salary > 100), subsetting by state and city, and computing mean sales with group by operations.

Detect and handle missing values in data using pandas, applying techniques to fill, replace, or drop missing data, and identify and manage outliers with simple plots.

Connect multiple data frames with pandas by concatenating and merging on common keys to build a unified dataset for machine learning, using inner, outer, left, and right joins.

Learn to use the Pandas apply function for element-wise transformations in a data frame, defining functions or lambdas to categorize sales by price (low, medium, high) and add new columns.

Learn to plot with Python using Pandas and Seaborn, building histograms and kernel density estimates to visualize distributions, using the diamonds dataset and basic plotting code.

Create a bar plot from a pandas table, using unique values in clarity and cut quality to compare categories, and import seaborn for visualization.

Build advanced bar plots with seaborn, customize colors, labels, and legend placement, and add annotations for clear, publication-ready data visualizations.

Load the dataset, filter for medal winners, and select the top five sports; then visualize gender-based medal counts to identify the sport with the most medals.

Explore advanced visualization in Python by building CO2 and GDP plots, cleaning data, merging tables, and analyzing the correlation between CO2 and GDP.

Explore advanced visualization with python using the bokeh library, building figures with column data sources, color mappers, and mapping dataframes to plotting sources.

Refine the visualization by placing the legend at the bottom right and labeling the axes with income per person and co2 emissions per person. Adjust the scale for clear plot.

Add a year slider to filter the dataframe's year column, update the plot of CO2 emission versus GDP in percentage, and enable hover details to show the country.

Create an animated scatterplot of GDP versus CO2 across countries, colored by region, with hover details and a year-based animation slider to visualize trends.

practice with Plotly demonstrates creating interactive visuals by building scatterplots of CO2 versus GDP, colored by region, with animation over year and a density contour for deeper insight.

Explore tokenization as the foundation of text analysis. Learn how breaking text into tokens and patterns like unigram and bigram supports nlp models with libraries handling token recognition.

Implement text analysis in python by importing the library, installing required packages, inputting sentences, and applying a tokenization function to split text into sentences and tokens.

Count word frequencies to reveal structure in the source text, and use regular expressions to extract emails, phone numbers, and hashtags while identifying the most common words.

Explore stemming and lemmatization for text analysis. See how stemming trims suffixes to a root form, while lemmatization uses dictionaries to map to lemmas.

Analyze text by tokenizing sentences and removing stopwords to create clean token data. Compare tokens against the original data and implement this workflow in Python with the tokenize module.

Learn text analysis implementation by tokenizing sentences and performing part-of-speech tagging to identify relationships between words. Create tokens, import necessary tools, and prepare tokenized data for downstream processing.

Learn to perform entity recognition in text using an English model, tokenization, and parsing to identify entities such as people, organizations, and laws.

Implement text analysis part 7 by applying entity recognition and natural language processing, loading data, visualizing results, and displaying insights in a concise paragraph.

Generate a word cloud to visualize the most frequent words, using a stop words list and Python libraries to customize font size and colors and display the result.

Keep practicing deep learning to become a successful deep learning engineer, set your own goals and challenges, and continue building models based on their data set.